1. Field of the Invention
The present invention relates to a method of creating registration signature data in a signature collation system. More particularly, the present invention relates to a method of creating registration signature data in a system in which attestation of a person is performed based on the dynamic characteristics of a signature.
2. Description of the Related Art
A handwritten character recognition method by which written characters are recognized has been utilized as an input method for word processors or a signature collation method for specifying a writer. Under a handwritten character recognition method which has already been in actual use as an input method, characters are input in the block style under specified constraints on the style of typeface, and the thus-input characters are converted into coordinate information. The thus-converted coordinate information is verified by comparison with coordinate information relating to character data which have been stored beforehand. As a result of collation, the characters are recognized as matched. If characters are carefully written in the block style at comparatively slow speed in the manner as previously described, the characters can be sufficiently recognized through use of only coordinate information because under such conditions each of the strokes of the characters becomes clear by virtue of visual feedback to the writer and hence the shape of the characters becomes stable.
In contrast, in a case where the character recognition method is applied to an input method which does not pose any restriction on the style of typeface at the time of input of characters or to a signature collation method, there must be recognized not only characters written in the block type but also cursively written characters. When characters are cursively written, writing motion becomes faster and does not involve any substantial visual feedback to the writer. In this case, the characters become less identifiable, and separation of a resultantly acquired pattern into strokes becomes difficult. This is because an expansion or contraction of the pattern in the direction of the time axis or in the direction of stroke, or the difference between the input pattern and a pre-registered pattern, becomes greater. For this reason, a matching rate is extremely low, rendering identification of characters difficult.
Another method is to enable recognition of characters without involving the separation of characters into strokes by application of time-series coordinate information and writing pressure. This method employs a pattern matching information stemming from variations in writing action.
In the dynamic processing matching technique, variations in the writing motion are corrected with regard to the time axis or the arc length axis through use of a warping function which minimizes a cumulative error between patterns to be checked. Patterns are matched with each other on the basis of the coordinates and writing pressure that have been corrected so as to compensate variations in the writing motion, thereby enabling recognition of cursively handwritten characters.
Verification based on the addition of writing pressure information to time-series coordinate information or normalization of input patterns by DP matching contributes to an improvement in the recognition rate of handwritten characters. However, in the case of application of the dynamic processing matching technique to recognition of cursively written characters or signature collation, a false signature may be erroneously recognized as a genuine signature. Therefore, in its present form, the dynamic processing matching technique cannot be put into practical use.
Japanese Patent No. 1,822,532 [Japanese Patent Publication (kokoku) No. 5-31798] entitled xe2x80x9cA Method of Recognizing Handwritten Characters Onlinexe2x80x9d describes a practical technique that is based on dynamic processing matching. Under this method, when the degree of difference between a registered pattern and an input pattern of handwritten characters is calculated by use of dynamic processing matching, time-series coordinate information and writing pressure information are simultaneously processed by the assignment of optimum weighting coefficients to the time-series coordinate information and writing pressure information. As a result, the difference is reduced, which in turn contributes to an improvement in the collation rate of authenticity and a reduction in processing time.
As mentioned previously, even in the case of unclear characters which cannot be separated into strokes, processing of the time-series coordinate information and writing pressure information relating to handwritten characters enables recognition of the characters. Further, even in the case of cursively handwritten characters, the characters can be recognized in practice, as a result of a further improvement in the dynamic processing matching technique that compensates variations in writing motion in order to correct cumulative errors.
In a static signature collation system, an image scanner or an image OCR is used as a tool for reading out characters. In contrast, in a dynamic signature collation system, a stylus pen is generally used. FIG. 1 shows a schematic view of a dynamic signature collation system utilizing a stylus pen. When characters are written on a tablet 2 through use of a stylus pen 1, signals representing characters are sent to a collation section, where signature collation is performed.
Such a tablet and stylus pen are important devices that affect ease of use. Therefore, recently these devices have been improved. For example, a tablet formed from a liquid-crystal panel and a wireless stylus pen having no signal cable have come into use. Further, in place of a piece of hardware dedicated to signature collation, a personal computer has come into use. In this case, signature collation is performed by software or a program.
The processing performed in the collation section is composed of three steps; i.e., pre-processing/normalization, character extraction, and identification/judgment. Information from the stylus pen includes relative coordinates (x, y) relative to the start point of a signature, and writing pressure p. Specifically, information as shown in Table 1 is obtained every unit time.
Since handwritten characters are not necessarily consistent, collation of a signature involves difficulty caused by variation in the direction of writing and in size, and hardware noise. The pre-processing/normalization removes these variations and noise and performs normalization in order to enable comparison with standard character patterns. Specifically, in the pre-processing, there are performed removal of excess series of points (sampling based on amount of relative movement), removal of random noise that depends on hand shake and resolution of a tablet (smoothing through load shift), removal of isolated data caused by erroneous operation of the tablet, and like operations.
After completion of the pre-processing, as shown in FIG. 2, the size and position of input characters are normalized. Subsequent to the above-described processing, characteristics of the characters are extracted, and identification/judgment processing is performed.
In Japan, seals have been accepted with absolute trust as means for personal authentication for settlements at financial institutions, agreements, and the like. By contrast, handwritten signatures have not been authorized as means for personal authentication as is the practice in western countries. However, since computerization and enhancement of communication techniques have advanced worldwide in various fields, personal authentication by means of seals has been found troublesome with regard to international transactions and future computerization in Japan.
Although seals are considered secure when a person imprints his/her own seal, seals are highly insecure in the field of computerized personal authentication, because in a computerized system a seal constitutes fixed data, and therefore there exists a high risk that the seal may be stolen and abused by a third person. To say nothing of the Internet, other computer networks have expanded worldwide, and computer communications are utilized in a wide range of fields such as formulation of business contracts, shopping, home banking, and bank settlement. The range of application of computer communications has been expanded. Seals, which are static identification tools, can no longer cope with such circumstances, and it is easily imagined that dynamic personal authentication based on a handwritten signature will become indispensable.
As described above, in recent years, there has rapidly developed a signature-based personal authentication technique that is practiced through use of a computer. However, computerized signature-based personal authentication involves many problems that remain to be solved.
In order to solve the problems, a personal authentication technique utilizing biometrics has recently been proposed. A basic principle states that the stricter the check of personal authentication, the more reliable the result. However, when the check is excessively strict, there arises a possibility that a person cannot be properly identified. A factor that causes such a problem is initial registration signature data that is used as a basis for collation of signature data.
Dynamic signatures or handwritten signatures vary among persons. Some persons can sign consistently and others cannot. A person signs differently depending on his/her mental state and environment. For example, when experiencing tension, a person generally writes characters at a slower speed with an increased writing pressure. If a signature made by a certain person is not identified as being signed by that person, this poses a problem; personal authentication that cannot identify the true signer is meaningless.
The action of writing characters is greatly affected by mental state, physical state, environment, and the like. Conventionally, registration data that are used as a reference for collation of signature data are input one time or a plurality of times. Conventionally, the input signature data are stored in their raw form as registration signature data. Therefore, if the signer forgets the style of the registered signature, or if the form of characters changes due to the conditions of the signer, the signer him/herself may be rejected as well as other persons.
In a conventional method of creating registration data, one or a plurality of sets of signature data are registered as registration signature data, and collation of input signature data is performed through use of the registration signature data. However, this method still has a drawback that collation cannot be performed reliably because of the above-described reasons.
Therefore, an object of the present invention is to provide a method of creating registration signature data which enables reliable collation in a computerized signing scheme, particularly in a dynamic signing scheme.
To accomplish the foregoing object, in the present invention, registration signature data that are used as a basis for signature collation are created through the steps of:
(1) averaging a plurality of signature data sets in order to calculate a candidate set of registration signature data;
(2) collating the candidate set of registration signature data with each signature data set;
(3) when no unacceptable discrepancy is found as a result of collation between the candidate set of registration signature data and the signature data sets, treating the candidate set of registration signature data as registration signature data; and
(4) when at least one unacceptable discrepancy is found as the result of collation between the candidate set of registration signature data and the signature data sets, repeating the steps (1) and (2) in order to find a candidate set of registration signature data for which no unacceptable discrepancy is found as a result of collation with the signature data sets, and treating the candidate set of registration signature data as registration signature data.
Instead of the above-described method in which data creation is repeated until a desired result is obtained, there can be employed a method comprising the steps of:
(1) averaging a plurality of signature data sets in order to calculate a candidate set of registration signature data;
(2) collating the candidate set of registration signature data with each signature data set;
(3) when no unacceptable discrepancy is found as a result of collation between the candidate set of registration signature data and the signature data sets, treating the candidate set of registration signature data as registration signature data; and
(4) when at least one unacceptable discrepancy is found as a result of collation between the candidate set of registration signature data and the signature data sets; inputting an additional signature data set; forming a plurality of groups each consisting of signature data sets selected from the increased number of signature data sets such that the number of signature data sets in each group is less than the total number of signature data sets; collating a candidate set of registration signature data that is obtained by averaging the signature data sets in each group with each signature data set; and treating as registration signature data the candidate set of registration signature data of a group that produces the best collation result.
Preferably, a signer is allowed to determine whether input signature data is to be used for creation of the registration signature data. Further, a practice mode is preferably provided for a person unfamiliar with signing in order to provide him/her with the opportunity to sign a plurality of times before making signatures for creation of the registration signature data.
In the present invention, since signatures of each person are averaged, reliable collation results can be obtained. Further, in the method in which a group that provides the best collation result is selected for registration, more consistent collation results can be obtained. The registration method of the present invention is particularly effective for creation of registration signature data in a signature collation scheme in which information other than the shape of characters is used, such as a dynamic signature collation scheme.